Meta·Software Engineer·Technical Phone Screen·Senior

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May 2026Remote

Summary

Meta SWE interview with a SQL debugging problem that was more involved than I expected. You get a live database and a broken query and have to both fix it and then critique the data it produces.

Questions Asked (1)

You're given a PostgreSQL environment with a schema and a supposedly AI-generated SQL query. Identify at least four critical issues with it (join types, fan-out, aggregation grain, etc.), fix it so it runs correctly, then inspect the output for data quality problems and update the query to address them.

Product Analytics & MetricsRoot Cause AnalysisTechnical Trade-offs

Author's notes

This one took me a while to wrap my head around because it's not just 'find the bug.' The query had a many-to-many join that was silently inflating row counts, and I almost missed it because the query actually ran fine.

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AI HintsAI Generated

Suggested Approach

Start by systematically reviewing the SQL query for common pitfalls such as incorrect join types, fan-out, and aggregation grain. Then, rewrite the query to fix these issues and execute it to inspect the output for anomalies like duplicates or missing data. Finally, refine the query to handle data quality problems, ensuring accurate and reliable results.

Pro tip: Always validate your assumptions about the data by running exploratory queries on row counts and distinct values before and after joins; this helps catch fan-out and grain issues early.

1. Identify Critical Issues

Review the query for join types (e.g., INNER vs LEFT), fan-out (one-to-many relationships causing row multiplication), aggregation grain (mismatch between GROUP BY and selected columns), and other common mistakes like missing filters or incorrect use of DISTINCT.

2. Fix the Query

Rewrite the query to correct the identified issues: use appropriate join types, pre-aggregate before joining to avoid fan-out, ensure GROUP BY includes all non-aggregated columns, and add necessary filters.

3. Execute and Inspect Output

Run the corrected query and examine the results for data quality problems such as unexpected NULLs, duplicate rows, or values that violate business rules (e.g., negative counts).

4. Address Data Quality Issues

Update the query to handle the identified data quality problems, e.g., by adding COALESCE for NULLs, using DISTINCT or window functions to deduplicate, or filtering out invalid records.

5. Validate and Explain

Re-run the final query to confirm it produces correct results, and be prepared to explain the changes made and their impact on the analysis.

Key Points to Mention

Join types: INNER JOIN may drop rows; LEFT JOIN may introduce NULLs; choose based on analysis needs.
Fan-out: One-to-many joins can multiply rows, leading to inflated aggregates; pre-aggregate or use subqueries.
Aggregation grain: Ensure GROUP BY matches the desired level of detail and includes all non-aggregated columns.
Data quality: Check for NULLs, duplicates, outliers, and inconsistent values; use functions like COALESCE, DISTINCT, or window functions to clean.
Performance: Consider indexing, avoiding SELECT *, and using EXPLAIN to optimize the query.
Business context: Align the query with the intended metric definition and ensure it answers the right question.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.